Abstract 5736: In silico analysis shows that PTEN loss and AR overexpression are associated with increased CD8+ T-cell and Treg density and earlier disease recurrence in prostate cancer
Notice bibliographique
Résumé
Abstract PTEN loss occurs in 20-30% prostate cancers (PCa), is associated with worse outcome, and regulates the type I interferon response through the interferon regulatory factor 3 (IRF3). Both PTEN loss and the overexpression of the androgen receptor (AR) promote castrate-resistant PCa leading to a more aggressive disease. To characterize the associations between PTEN loss and AR expression in immune cell infiltration in the tumor microenvironment (TME), we conducted an in silico analysis in two PCa cohorts. RNAseq and gene expression array data were imputed in CIBERSORT to evaluate the relative frequency of 22 immune infiltrating cells in the TME. PTEN was lost in 19.7% (97/491) and 19.8% (26/131) in the TCGA and MSKCC cohorts, respectively. AR was overexpressed in 56% (279/491) and 54% (71/131) of the TCGA and MSKCC cohorts, respectively. Remarkably, AR overexpression was associated with decreased CD8+ T-cells infiltration in both the TCGA (p<0.0001) and MSKCC (p=0.001) cohorts. In addition we found that PTEN loss was associated with increased Treg infiltration in both the TCGA (p=0.002) and MSKCC (p<0.0001) cohorts. PTEN loss was also associated with low plasma cell infiltration in both the TCGA and MSKCC cohorts (p=0.001 and p=0.002, respectively). More detailed analysis of the MSKCC cohort showed that PTEN loss correlated with increased CD8+ T-cell density in the TME (p<0.0001). When we compared the immune cell infiltration profile from the primary and metastatic tumors in the MSKCC cohort, we found an increased infiltration of memory B-cell, Treg, and active dendritic cells (p<0.0001, p=0.005, and p=0.002, respectively) in the metastatic tumors. We then investigated the association between PTEN and AR expression with the most common immunotherapeutic targets. We found that PTEN loss was associated with increased PD-1 expression in MSKCC cohort (p<0.0001) and with reduced PD-L1 expression in the TCGA cohort (p=0.006). Increased AR expression correlated with reduced PD-1 expression in both TCGA and MSKCC cohorts (p<0.0001) and with reduced CTLA-4 expression in TCGA cohort (p=0.009). There was a positive association between PD-1 and CTLA-4 expression with CD8+ T-cell and Treg infiltration in both cohorts. Log-rank analysis showed that PTEN loss and high Treg infiltration predicted earlier disease recurrence in both the TCGA and MSKCC cohorts (p=0.021 and p=0.027, respectively). In addition, combined PTEN loss and increased CD8+ T-cell infiltration promotes earlier disease recurrence in both the TCGA and MSKCC cohorts (p=0.05 and p=0.029, respectively). These findings imply that the immune cell profile in the TME can be influenced by changes in the expression of PTEN and AR, suggesting that novel immunotherapies may promote better responses in castrate-resistant PCa. Citation Format: Thiago Vidotto, Madhuri Koti, Jeremy A. Squire. In silico analysis shows that PTEN loss and AR overexpression are associated with increased CD8+ T-cell and Treg density and earlier disease recurrence in prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5736.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».